The Centre for Digital Music (C4DM) Research Group Projects

Smart phone apps have shown that there is a demand for easy-to-use IT functions, yet the potential scope and impact of easy to use IT is much greater. This network will promote research into providing access to a wider range of IT facilities that are simple to use, while users need know only that these services are available and safe to use, not where they come from and how they are provided. However, it is not enough to provide the services and the means of using them securely. The network will develop the understanding of the human aspects of such services: the barriers, perceived as well as real, that inhibit new users of the services. We rely implicitly on other things, such as our cars and our electricity supply, so a major Network facet will be in the area of "how can we encourage the same level of trust in IT services"; the network will tackle the the issues from a user perspective. An analogy with the traditional utilities of Power and Water is interesting. With Water we do care about the actual product as we do in part consume it directly! We also use it to drive a range of other processes. With electricity, while we do measure the amount we consume, most people don't actually think in terms of the current flowing though their mains circuits but in terms of boiling the kettle or switch on the TV - it is the service that the power enables that we focus on (at least until the power goes out). For IT this is even more true; for almost all the time we are not concerned with the infrastructure but with the services that this infrastructure can provide. Therefore there will be a strong focus in the network on such the provision of such services and also their operational management protocols. IT drives and is driven by technology, inescapably hardware is also IT and that it is now commoditised and therefore utilitarian and has the capability to alter dramatically the way people interact with the space around them by merging physical and digital spaces in a cost effective manner. Smart spaces, responsive environments, and location dependent services can fundamentally alter interactions between people as well as the space around them. These services depend at their heart on information and data. The ITaaU Network+ will therefore interact closely with governmental and research council initiatives on Open Data, and more generally on use and re-use of data, made feasible by the adoption of open and linked data standards, and new services which can be developed as more data becomes available.

Most digital audio effects, whether implemented as plug-ins for mixers and audio editors or implemented as offline audio signal processing techniques, typically take a single channel as input and produce a single channel as output. The exceptions to this are fairly simple, such as ducking (which modifies one channel based on the level of another) and stereo effects (which produce two output channels). The goal of this research is to develop MIMO (Multiple Input, Multiple Output) audio effects. These can be used to create different versions of a multichannel recording which are tailored to different listeners, or to modify channels based on the content in many other channels. Applications include live sound, where a customized mix may be fed back to each performer. Current audio editors do not offer the ability to create plug-ins which may analyse or modify the multi-channel content. Thus this work will also involve either submitting modifications to an open source audio editor, or creating your own with the required functionality. Recommended skills Knowledge of audio signal processing Programming skills Knowledge of music and/or sound engineering

This proposal stems directly from the EPSRC Workshop held on 20 & 21 October 2010 "ICT Research - The Next Decade". It seeks to address the challenge of the navigation of time-based media collections and items throughout the content life-cycle, from creation to consumption. It will achieve this by establishing an open network of researchers from across academia and industry, who engage in workshops, sandpits and, most importantly, feasibility or path-finder mini-projects. These mini-projects have the aims not only of performing leading edge, early stage research that will lead on to larger proposals, but also of building a critical mass of researchers, whose expectation is to tackle significant challenges by collaborating. Other elements of this project are to create a Landscape document for the field, develop appropriate ontologies for capturing media semantics, present results through a diverse range of channels and summarise the findings of the project, including a Roadmap. The research agenda is based on five premises: 1. Content-related metadata is an effective and scalable approach exemplified in this domain and applicable to future large scale, automated and interactive information systems; 2. The point of creation is the best time and place to collect (and compute) metadata; 3. The best way to represent this metadata is one that is amenable to knowledge processing and management, linked data strategies and logical inference; 4. Significant challenges require a cross-disciplinary approach, ranging from fundamental theory to applied research set in the context of a real problem; and 5. The UK is supremely placed with the world-leading skills and experience to be a world-beating authority in an area of intellectual and societal/commercial benefit. This proposal deliberately does not address related problems of navigation through legacy content, nor of Rights Management, as these are already embedded in the research landscape. Instead, we concentrate on the production of future media items. The issues raised and investigated by this proposal are pertinent not only to EPSRC, but also to ESRC, AHRC, JISC and TSB, and with particular relevance to the Digital Economy. The applications of such ideas span all the different time-based media, including music, drama, documentary, film, texts and so on. In order to advance the field, this project will bring together acknowledged experts from across UK academia in a diverse range of disciplines, including Semantic Web experts, Signal Processing experts, Video experts, Performance experts and more. The project aims to form a network and a critical mass of expertise by a series of interventions that will also include industrial collaborators (assisted by the TSB Creative Industries Knowledge Transfer Network). Network activities include workshops and sandpits, as well as collaborative small scale research projects, each typically of 6 months duration with 2 or 3 participant universities. The outcomes of the project include: Research and Impact Roadmaps; a well-connected community of researchers engineers, creatives, content producers and funders; commercial and full-fledged research proposals; research publications; and specific impact activities at world leading Broadcast and Media conventions.

Musical Instrument Identification is one of the more well-known tasks in musical signal processing. There are standard procedures and techniques for this, yet the classification rates are often very poor. The techniques are often focused on single instrument sounds, and fail when applied on testbeds with notably different qualities than the training data. Furthermore, they are rarely adapted to the task of Musical Instrument Segmentation, and thus cannot be easily used to, for instance, identify guitar solos in popular recordings. Researchers at the Centre for Digital Music have developed more sophisticated instrument identification techniques that focus on the spectral content produced by each instrument. These have yielded exceptionally high classification rates on standard testbeds. The goal of this research would be to assess and implement these techniques, and then to adapt them to the task of Instrument Segmentation and Labelling, with an emphasis on diverse testbeds. The planned outcome of this research is a clear advancement in the state of the art of the performance and usability of instrument recognition techniques. Prerequisites for this are programming skills and an understanding of musical signal analysis and processing. This project will also require frequent interaction with other researchers Recommended skills Knowledge of audio signal processing and machine learning techniques Programming skills